Overview

Dataset statistics

Number of variables41
Number of observations10005
Missing cells0
Missing cells (%)0.0%
Duplicate rows1965
Duplicate rows (%)19.6%
Total size in memory1.9 MiB
Average record size in memory203.0 B

Variable types

Numeric21
Categorical20

Warnings

Dataset has 1965 (19.6%) duplicate rows Duplicates
Sub channel_IBD is highly correlated with Sub channel_NACSHigh correlation
Sub channel_NACS is highly correlated with Sub channel_IBDHigh correlation
Sub channel_NACS is highly correlated with Sub channel_IBDHigh correlation
Sub channel_IBD is highly correlated with Sub channel_NACSHigh correlation
no_of_Redemption_12M_1 is highly skewed (γ1 = 52.12310105) Skewed
no_of_sales_12M_10K is highly skewed (γ1 = 36.02830336) Skewed
no_of_Redemption_12M_10K is highly skewed (γ1 = 42.62915028) Skewed
AUM is highly skewed (γ1 = 45.17108772) Skewed
sales_curr is highly skewed (γ1 = 27.7623075) Skewed
sales_12M is highly skewed (γ1 = 25.89467679) Skewed
redemption_curr is highly skewed (γ1 = -39.74234212) Skewed
redemption_12M is highly skewed (γ1 = -27.16514523) Skewed
no_of_sales_12M_1 has 5242 (52.4%) zeros Zeros
no_of_Redemption_12M_1 has 4644 (46.4%) zeros Zeros
no_of_sales_12M_10K has 7293 (72.9%) zeros Zeros
no_of_Redemption_12M_10K has 7029 (70.3%) zeros Zeros
no_of_funds_sold_12M_1 has 5242 (52.4%) zeros Zeros
no_of_funds_redeemed_12M_1 has 4644 (46.4%) zeros Zeros
no_of_fund_sales_12M_10K has 7293 (72.9%) zeros Zeros
no_of_funds_Redemption_12M_10K has 7029 (70.3%) zeros Zeros
no_of_assetclass_sold_12M_1 has 5242 (52.4%) zeros Zeros
no_of_assetclass_redeemed_12M_1 has 4644 (46.4%) zeros Zeros
no_of_assetclass_sales_12M_10K has 7293 (72.9%) zeros Zeros
No_of_fund_curr has 3822 (38.2%) zeros Zeros
No_of_asset_curr has 4426 (44.2%) zeros Zeros
AUM has 4989 (49.9%) zeros Zeros
sales_curr has 7580 (75.8%) zeros Zeros
sales_12M has 5248 (52.5%) zeros Zeros
redemption_curr has 7430 (74.3%) zeros Zeros
redemption_12M has 4628 (46.3%) zeros Zeros
new_Fund_added_12M has 7310 (73.1%) zeros Zeros
sales_2019 has 4935 (49.3%) zeros Zeros
new_fund_2019 has 7484 (74.8%) zeros Zeros

Reproduction

Analysis started2021-02-13 10:51:25.454930
Analysis finished2021-02-13 10:52:44.735945
Duration1 minute and 19.28 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

no_of_sales_12M_1
Real number (ℝ≥0)

ZEROS

Distinct343
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.08215892
Minimum0
Maximum4395
Zeros5242
Zeros (%)52.4%
Memory size156.3 KiB
2021-02-13T11:52:44.940827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile88
Maximum4395
Range4395
Interquartile range (IQR)8

Descriptive statistics

Standard deviation98.44040858
Coefficient of variation (CV)4.901883755
Kurtosis608.0439242
Mean20.08215892
Median Absolute Deviation (MAD)0
Skewness19.30971489
Sum200922
Variance9690.514041
MonotocityNot monotonic
2021-02-13T11:52:45.096485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05242
52.4%
1671
 
6.7%
2413
 
4.1%
3325
 
3.2%
4244
 
2.4%
5210
 
2.1%
6145
 
1.4%
7139
 
1.4%
12135
 
1.3%
9133
 
1.3%
Other values (333)2348
23.5%
ValueCountFrequency (%)
05242
52.4%
1671
 
6.7%
2413
 
4.1%
3325
 
3.2%
4244
 
2.4%
ValueCountFrequency (%)
43951
< 0.1%
32381
< 0.1%
24471
< 0.1%
19351
< 0.1%
17501
< 0.1%

no_of_Redemption_12M_1
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct338
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.97641179
Minimum0
Maximum12152
Zeros4644
Zeros (%)46.4%
Memory size156.3 KiB
2021-02-13T11:52:45.249580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile83
Maximum12152
Range12152
Interquartile range (IQR)8

Descriptive statistics

Standard deviation191.5516344
Coefficient of variation (CV)9.131763633
Kurtosis3201.595161
Mean20.97641179
Median Absolute Deviation (MAD)1
Skewness52.12310105
Sum209869
Variance36692.02863
MonotocityNot monotonic
2021-02-13T11:52:45.427072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04644
46.4%
11002
 
10.0%
2554
 
5.5%
3384
 
3.8%
4290
 
2.9%
5250
 
2.5%
6193
 
1.9%
7147
 
1.5%
8137
 
1.4%
9129
 
1.3%
Other values (328)2275
22.7%
ValueCountFrequency (%)
04644
46.4%
11002
 
10.0%
2554
 
5.5%
3384
 
3.8%
4290
 
2.9%
ValueCountFrequency (%)
121521
< 0.1%
120731
< 0.1%
39391
< 0.1%
32501
< 0.1%
15471
< 0.1%

no_of_sales_12M_10K
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct103
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.420089955
Minimum0
Maximum986
Zeros7293
Zeros (%)72.9%
Memory size156.3 KiB
2021-02-13T11:52:45.595065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum986
Range986
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.53480072
Coefficient of variation (CV)6.005892753
Kurtosis2165.850275
Mean2.420089955
Median Absolute Deviation (MAD)0
Skewness36.02830336
Sum24213
Variance211.260432
MonotocityNot monotonic
2021-02-13T11:52:45.760021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07293
72.9%
1807
 
8.1%
2466
 
4.7%
3241
 
2.4%
4183
 
1.8%
5149
 
1.5%
6100
 
1.0%
785
 
0.8%
961
 
0.6%
859
 
0.6%
Other values (93)561
 
5.6%
ValueCountFrequency (%)
07293
72.9%
1807
 
8.1%
2466
 
4.7%
3241
 
2.4%
4183
 
1.8%
ValueCountFrequency (%)
9861
< 0.1%
3301
< 0.1%
2891
< 0.1%
2171
< 0.1%
2061
< 0.1%

no_of_Redemption_12M_10K
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.856371814
Minimum0
Maximum883
Zeros7029
Zeros (%)70.3%
Memory size156.3 KiB
2021-02-13T11:52:45.925366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8
Maximum883
Range883
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.23370734
Coefficient of variation (CV)6.590116939
Kurtosis2775.260031
Mean1.856371814
Median Absolute Deviation (MAD)0
Skewness42.62915028
Sum18573
Variance149.6635952
MonotocityNot monotonic
2021-02-13T11:52:46.083762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07029
70.3%
11149
 
11.5%
2529
 
5.3%
3295
 
2.9%
4175
 
1.7%
5136
 
1.4%
690
 
0.9%
774
 
0.7%
871
 
0.7%
961
 
0.6%
Other values (74)396
 
4.0%
ValueCountFrequency (%)
07029
70.3%
11149
 
11.5%
2529
 
5.3%
3295
 
2.9%
4175
 
1.7%
ValueCountFrequency (%)
8831
< 0.1%
2831
< 0.1%
2821
< 0.1%
2051
< 0.1%
1821
< 0.1%

no_of_funds_sold_12M_1
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.35992004
Minimum0
Maximum32
Zeros5242
Zeros (%)52.4%
Memory size156.3 KiB
2021-02-13T11:52:46.231134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.379879674
Coefficient of variation (CV)1.750014416
Kurtosis15.74681138
Mean1.35992004
Median Absolute Deviation (MAD)0
Skewness3.242818486
Sum13606
Variance5.663827263
MonotocityNot monotonic
2021-02-13T11:52:46.384161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
05242
52.4%
11946
 
19.5%
21045
 
10.4%
3591
 
5.9%
4365
 
3.6%
5213
 
2.1%
6178
 
1.8%
7110
 
1.1%
870
 
0.7%
964
 
0.6%
Other values (16)181
 
1.8%
ValueCountFrequency (%)
05242
52.4%
11946
 
19.5%
21045
 
10.4%
3591
 
5.9%
4365
 
3.6%
ValueCountFrequency (%)
321
< 0.1%
271
< 0.1%
231
< 0.1%
222
< 0.1%
212
< 0.1%

no_of_funds_redeemed_12M_1
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.544327836
Minimum0
Maximum33
Zeros4644
Zeros (%)46.4%
Memory size156.3 KiB
2021-02-13T11:52:47.029595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.467776953
Coefficient of variation (CV)1.597961842
Kurtosis15.22035199
Mean1.544327836
Median Absolute Deviation (MAD)1
Skewness3.121370058
Sum15451
Variance6.089923091
MonotocityNot monotonic
2021-02-13T11:52:47.164412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
04644
46.4%
12062
20.6%
21254
 
12.5%
3676
 
6.8%
4450
 
4.5%
5271
 
2.7%
6196
 
2.0%
7113
 
1.1%
876
 
0.8%
965
 
0.6%
Other values (16)198
 
2.0%
ValueCountFrequency (%)
04644
46.4%
12062
20.6%
21254
 
12.5%
3676
 
6.8%
4450
 
4.5%
ValueCountFrequency (%)
331
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
231
 
< 0.1%
217
0.1%

no_of_fund_sales_12M_10K
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5703148426
Minimum0
Maximum19
Zeros7293
Zeros (%)72.9%
Memory size156.3 KiB
2021-02-13T11:52:47.298474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.329913863
Coefficient of variation (CV)2.331894181
Kurtosis24.80068573
Mean0.5703148426
Median Absolute Deviation (MAD)0
Skewness4.098908602
Sum5706
Variance1.768670882
MonotocityNot monotonic
2021-02-13T11:52:47.417044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
07293
72.9%
11424
 
14.2%
2616
 
6.2%
3300
 
3.0%
4144
 
1.4%
575
 
0.7%
654
 
0.5%
736
 
0.4%
821
 
0.2%
913
 
0.1%
Other values (7)29
 
0.3%
ValueCountFrequency (%)
07293
72.9%
11424
 
14.2%
2616
 
6.2%
3300
 
3.0%
4144
 
1.4%
ValueCountFrequency (%)
191
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
132
 
< 0.1%
126
0.1%

no_of_funds_Redemption_12M_10K
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.607896052
Minimum0
Maximum21
Zeros7029
Zeros (%)70.3%
Memory size156.3 KiB
2021-02-13T11:52:47.548883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.358727039
Coefficient of variation (CV)2.235130553
Kurtosis34.15335594
Mean0.607896052
Median Absolute Deviation (MAD)0
Skewness4.47196549
Sum6082
Variance1.846139166
MonotocityNot monotonic
2021-02-13T11:52:47.664321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
07029
70.3%
11630
 
16.3%
2645
 
6.4%
3284
 
2.8%
4197
 
2.0%
581
 
0.8%
656
 
0.6%
730
 
0.3%
813
 
0.1%
1011
 
0.1%
Other values (9)29
 
0.3%
ValueCountFrequency (%)
07029
70.3%
11630
 
16.3%
2645
 
6.4%
3284
 
2.8%
4197
 
2.0%
ValueCountFrequency (%)
212
< 0.1%
201
< 0.1%
172
< 0.1%
161
< 0.1%
151
< 0.1%

no_of_assetclass_sold_12M_1
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7862068966
Minimum0
Maximum5
Zeros5242
Zeros (%)52.4%
Memory size156.3 KiB
2021-02-13T11:52:47.784136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.004771531
Coefficient of variation (CV)1.277998877
Kurtosis0.6534995107
Mean0.7862068966
Median Absolute Deviation (MAD)0
Skewness1.185360917
Sum7866
Variance1.009565829
MonotocityNot monotonic
2021-02-13T11:52:47.896266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
05242
52.4%
12615
26.1%
21336
 
13.4%
3677
 
6.8%
4127
 
1.3%
58
 
0.1%
ValueCountFrequency (%)
05242
52.4%
12615
26.1%
21336
 
13.4%
3677
 
6.8%
4127
 
1.3%
ValueCountFrequency (%)
58
 
0.1%
4127
 
1.3%
3677
 
6.8%
21336
13.4%
12615
26.1%

no_of_assetclass_redeemed_12M_1
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9067466267
Minimum0
Maximum5
Zeros4644
Zeros (%)46.4%
Memory size156.3 KiB
2021-02-13T11:52:48.007026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.046786108
Coefficient of variation (CV)1.15444169
Kurtosis0.1890241434
Mean0.9067466267
Median Absolute Deviation (MAD)1
Skewness0.9957527068
Sum9072
Variance1.095761156
MonotocityNot monotonic
2021-02-13T11:52:48.122485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
04644
46.4%
12805
28.0%
21585
 
15.8%
3795
 
7.9%
4168
 
1.7%
58
 
0.1%
ValueCountFrequency (%)
04644
46.4%
12805
28.0%
21585
 
15.8%
3795
 
7.9%
4168
 
1.7%
ValueCountFrequency (%)
58
 
0.1%
4168
 
1.7%
3795
 
7.9%
21585
15.8%
12805
28.0%

no_of_assetclass_sales_12M_10K
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3787106447
Minimum0
Maximum5
Zeros7293
Zeros (%)72.9%
Memory size156.3 KiB
2021-02-13T11:52:48.226322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.709087734
Coefficient of variation (CV)1.872373391
Kurtosis4.297082837
Mean0.3787106447
Median Absolute Deviation (MAD)0
Skewness2.058515187
Sum3789
Variance0.5028054146
MonotocityNot monotonic
2021-02-13T11:52:48.342854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07293
72.9%
11861
 
18.6%
2659
 
6.6%
3159
 
1.6%
432
 
0.3%
51
 
< 0.1%
ValueCountFrequency (%)
07293
72.9%
11861
 
18.6%
2659
 
6.6%
3159
 
1.6%
432
 
0.3%
ValueCountFrequency (%)
51
 
< 0.1%
432
 
0.3%
3159
 
1.6%
2659
 
6.6%
11861
18.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0.0
7029 
1.0
2012 
2.0
716 
3.0
 
212
4.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30015
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07029
70.3%
1.02012
 
20.1%
2.0716
 
7.2%
3.0212
 
2.1%
4.036
 
0.4%
2021-02-13T11:52:48.612832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:48.699858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07029
70.3%
1.02012
 
20.1%
2.0716
 
7.2%
3.0212
 
2.1%
4.036
 
0.4%

Most occurring characters

ValueCountFrequency (%)
017034
56.8%
.10005
33.3%
12012
 
6.7%
2716
 
2.4%
3212
 
0.7%
436
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20010
66.7%
Other Punctuation10005
33.3%

Most frequent character per category

ValueCountFrequency (%)
017034
85.1%
12012
 
10.1%
2716
 
3.6%
3212
 
1.1%
436
 
0.2%
ValueCountFrequency (%)
.10005
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30015
100.0%

Most frequent character per script

ValueCountFrequency (%)
017034
56.8%
.10005
33.3%
12012
 
6.7%
2716
 
2.4%
3212
 
0.7%
436
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30015
100.0%

Most frequent character per block

ValueCountFrequency (%)
017034
56.8%
.10005
33.3%
12012
 
6.7%
2716
 
2.4%
3212
 
0.7%
436
 
0.1%

No_of_fund_curr
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.144127936
Minimum0
Maximum32
Zeros3822
Zeros (%)38.2%
Memory size156.3 KiB
2021-02-13T11:52:48.819686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.136571619
Coefficient of variation (CV)1.462865889
Kurtosis9.284846655
Mean2.144127936
Median Absolute Deviation (MAD)1
Skewness2.565274298
Sum21452
Variance9.838081519
MonotocityNot monotonic
2021-02-13T11:52:48.953669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
03822
38.2%
12204
22.0%
21174
 
11.7%
3745
 
7.4%
4525
 
5.2%
5373
 
3.7%
6271
 
2.7%
7192
 
1.9%
8169
 
1.7%
9125
 
1.2%
Other values (20)405
 
4.0%
ValueCountFrequency (%)
03822
38.2%
12204
22.0%
21174
 
11.7%
3745
 
7.4%
4525
 
5.2%
ValueCountFrequency (%)
321
 
< 0.1%
311
 
< 0.1%
281
 
< 0.1%
273
< 0.1%
261
 
< 0.1%

No_of_asset_curr
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92003998
Minimum0
Maximum6
Zeros4426
Zeros (%)44.2%
Memory size156.3 KiB
2021-02-13T11:52:49.081891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.042385999
Coefficient of variation (CV)1.132979024
Kurtosis0.7789258605
Mean0.92003998
Median Absolute Deviation (MAD)1
Skewness1.098522688
Sum9205
Variance1.086568571
MonotocityNot monotonic
2021-02-13T11:52:49.184042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04426
44.2%
13099
31.0%
21595
 
15.9%
3656
 
6.6%
4199
 
2.0%
528
 
0.3%
62
 
< 0.1%
ValueCountFrequency (%)
04426
44.2%
13099
31.0%
21595
 
15.9%
3656
 
6.6%
4199
 
2.0%
ValueCountFrequency (%)
62
 
< 0.1%
528
 
0.3%
4199
 
2.0%
3656
6.6%
21595
15.9%

AUM
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct4987
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485000.2156
Minimum0
Maximum223241111.2
Zeros4989
Zeros (%)49.9%
Memory size156.3 KiB
2021-02-13T11:52:49.331833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.918116
Q3148411.4636
95-th percentile2144665.761
Maximum223241111.2
Range223241111.2
Interquartile range (IQR)148411.4636

Descriptive statistics

Standard deviation2993438.353
Coefficient of variation (CV)6.172035098
Kurtosis3129.769006
Mean485000.2156
Median Absolute Deviation (MAD)23.918116
Skewness45.17108772
Sum4852427157
Variance8.960673173 × 1012
MonotocityNot monotonic
2021-02-13T11:52:49.495427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04989
49.9%
250004
 
< 0.1%
1000004
 
< 0.1%
500003
 
< 0.1%
100003
 
< 0.1%
150003
 
< 0.1%
60003
 
< 0.1%
97003
 
< 0.1%
2000002
 
< 0.1%
95002
 
< 0.1%
Other values (4977)4989
49.9%
ValueCountFrequency (%)
04989
49.9%
0.041
 
< 0.1%
0.241
 
< 0.1%
1.281
 
< 0.1%
1.4851
 
< 0.1%
ValueCountFrequency (%)
223241111.21
< 0.1%
79736965.081
< 0.1%
37991686.21
< 0.1%
33271675.071
< 0.1%
32227317.41
< 0.1%

sales_curr
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2315
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17293.53807
Minimum0
Maximum9639535
Zeros7580
Zeros (%)75.8%
Memory size156.3 KiB
2021-02-13T11:52:49.663216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile35000
Maximum9639535
Range9639535
Interquartile range (IQR)0

Descriptive statistics

Standard deviation194098.5023
Coefficient of variation (CV)11.22375893
Kurtosis991.702126
Mean17293.53807
Median Absolute Deviation (MAD)0
Skewness27.7623075
Sum173021848.3
Variance3.767422861 × 1010
MonotocityNot monotonic
2021-02-13T11:52:49.833455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07580
75.8%
250007
 
0.1%
350006
 
0.1%
506
 
0.1%
2506
 
0.1%
500005
 
< 0.1%
1505
 
< 0.1%
1000005
 
< 0.1%
100005
 
< 0.1%
3004
 
< 0.1%
Other values (2305)2376
 
23.7%
ValueCountFrequency (%)
07580
75.8%
0.511
 
< 0.1%
2.561
 
< 0.1%
31
 
< 0.1%
4.7776221
 
< 0.1%
ValueCountFrequency (%)
96395351
< 0.1%
6119333.411
< 0.1%
6012961.991
< 0.1%
5273295.2171
< 0.1%
5270473.6451
< 0.1%

sales_12M
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct4622
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173095.4214
Minimum0
Maximum54346496.96
Zeros5248
Zeros (%)52.5%
Memory size156.3 KiB
2021-02-13T11:52:49.999536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331557.195
95-th percentile689511.4126
Maximum54346496.96
Range54346496.96
Interquartile range (IQR)31557.195

Descriptive statistics

Standard deviation1129623.71
Coefficient of variation (CV)6.526017273
Kurtosis1000.896648
Mean173095.4214
Median Absolute Deviation (MAD)0
Skewness25.89467679
Sum1731819691
Variance1.276049725 × 1012
MonotocityNot monotonic
2021-02-13T11:52:50.169949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05248
52.5%
2000014
 
0.1%
2500011
 
0.1%
5000010
 
0.1%
100009
 
0.1%
1000008
 
0.1%
50007
 
0.1%
150006
 
0.1%
350006
 
0.1%
60006
 
0.1%
Other values (4612)4680
46.8%
ValueCountFrequency (%)
05248
52.5%
0.0151
 
< 0.1%
0.181
 
< 0.1%
0.481
 
< 0.1%
1.061
 
< 0.1%
ValueCountFrequency (%)
54346496.961
< 0.1%
49288520.51
< 0.1%
29963424.51
< 0.1%
275104471
< 0.1%
17934270.481
< 0.1%

redemption_curr
Real number (ℝ)

SKEWED
ZEROS

Distinct2447
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-18525.40367
Minimum-13208846.26
Maximum447745.8104
Zeros7430
Zeros (%)74.3%
Memory size156.3 KiB
2021-02-13T11:52:50.334401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-13208846.26
5-th percentile-54530.12
Q1-11.03
median0
Q30
95-th percentile0
Maximum447745.8104
Range13656592.07
Interquartile range (IQR)11.03

Descriptive statistics

Standard deviation196813.9931
Coefficient of variation (CV)-10.62400564
Kurtosis2239.070837
Mean-18525.40367
Median Absolute Deviation (MAD)0
Skewness-39.74234212
Sum-185346663.7
Variance3.873574789 × 1010
MonotocityNot monotonic
2021-02-13T11:52:50.493480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07430
74.3%
-1516
 
0.2%
-3010
 
0.1%
-10009
 
0.1%
-15008
 
0.1%
-5008
 
0.1%
-25007
 
0.1%
-40006
 
0.1%
-50006
 
0.1%
-60005
 
< 0.1%
Other values (2437)2500
 
25.0%
ValueCountFrequency (%)
-13208846.261
< 0.1%
-5664250.1751
< 0.1%
-5283473.51
< 0.1%
-5244763.261
< 0.1%
-4610505.081
< 0.1%
ValueCountFrequency (%)
447745.81041
 
< 0.1%
39.881
 
< 0.1%
0.51
 
< 0.1%
07430
74.3%
-0.011
 
< 0.1%

redemption_12M
Real number (ℝ)

SKEWED
ZEROS

Distinct5266
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-123897.3797
Minimum-46703684.94
Maximum748167.23
Zeros4628
Zeros (%)46.3%
Memory size156.3 KiB
2021-02-13T11:52:50.663730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-46703684.94
5-th percentile-462443.614
Q1-33331.9998
median-514.795
Q30
95-th percentile0
Maximum748167.23
Range47451852.16
Interquartile range (IQR)33331.9998

Descriptive statistics

Standard deviation890215.0835
Coefficient of variation (CV)-7.185100167
Kurtosis1058.102763
Mean-123897.3797
Median Absolute Deviation (MAD)514.795
Skewness-27.16514523
Sum-1239593284
Variance7.924828949 × 1011
MonotocityNot monotonic
2021-02-13T11:52:50.824360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
46.3%
-1510
 
0.1%
-1000010
 
0.1%
-309
 
0.1%
-200006
 
0.1%
-30006
 
0.1%
-500005
 
< 0.1%
-5005
 
< 0.1%
-40005
 
< 0.1%
-50005
 
< 0.1%
Other values (5256)5316
53.1%
ValueCountFrequency (%)
-46703684.941
< 0.1%
-30732377.971
< 0.1%
-25230422.691
< 0.1%
-20268146.981
< 0.1%
-19437733.321
< 0.1%
ValueCountFrequency (%)
748167.231
 
< 0.1%
1968051
 
< 0.1%
61307.278351
 
< 0.1%
0.961
 
< 0.1%
04628
46.3%

new_Fund_added_12M
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4925537231
Minimum0
Maximum16
Zeros7310
Zeros (%)73.1%
Memory size156.3 KiB
2021-02-13T11:52:50.961011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.199372389
Coefficient of variation (CV)2.43500827
Kurtosis34.28015658
Mean0.4925537231
Median Absolute Deviation (MAD)0
Skewness4.88793048
Sum4928
Variance1.438494128
MonotocityNot monotonic
2021-02-13T11:52:51.085955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
07310
73.1%
11690
 
16.9%
2547
 
5.5%
3230
 
2.3%
472
 
0.7%
550
 
0.5%
623
 
0.2%
720
 
0.2%
918
 
0.2%
1015
 
0.1%
Other values (6)30
 
0.3%
ValueCountFrequency (%)
07310
73.1%
11690
 
16.9%
2547
 
5.5%
3230
 
2.3%
472
 
0.7%
ValueCountFrequency (%)
161
 
< 0.1%
143
< 0.1%
133
< 0.1%
126
0.1%
117
0.1%

sales_2019
Real number (ℝ≥0)

ZEROS

Distinct4911
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214009.8062
Minimum0
Maximum48676376.97
Zeros4935
Zeros (%)49.3%
Memory size156.3 KiB
2021-02-13T11:52:51.243496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89.89
Q345279.95
95-th percentile910365.526
Maximum48676376.97
Range48676376.97
Interquartile range (IQR)45279.95

Descriptive statistics

Standard deviation1155078.948
Coefficient of variation (CV)5.397317855
Kurtosis482.3381763
Mean214009.8062
Median Absolute Deviation (MAD)89.89
Skewness17.32606132
Sum2141168111
Variance1.334207376 × 1012
MonotocityNot monotonic
2021-02-13T11:52:51.415966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04935
49.3%
2500017
 
0.2%
5000011
 
0.1%
200009
 
0.1%
100007
 
0.1%
1250006
 
0.1%
1000006
 
0.1%
400006
 
0.1%
50006
 
0.1%
750005
 
< 0.1%
Other values (4901)4997
49.9%
ValueCountFrequency (%)
04935
49.3%
0.011
 
< 0.1%
0.41
 
< 0.1%
0.421
 
< 0.1%
0.8551
 
< 0.1%
ValueCountFrequency (%)
48676376.971
< 0.1%
31360821.61
< 0.1%
28091393.741
< 0.1%
26884452.71
< 0.1%
19140606.521
< 0.1%

new_fund_2019
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4349825087
Minimum0
Maximum20
Zeros7484
Zeros (%)74.8%
Memory size156.3 KiB
2021-02-13T11:52:51.559531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.111216312
Coefficient of variation (CV)2.554622978
Kurtosis47.68455821
Mean0.4349825087
Median Absolute Deviation (MAD)0
Skewness5.572591958
Sum4352
Variance1.234801692
MonotocityNot monotonic
2021-02-13T11:52:51.681845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
07484
74.8%
11687
 
16.9%
2461
 
4.6%
3184
 
1.8%
475
 
0.7%
532
 
0.3%
618
 
0.2%
1012
 
0.1%
1111
 
0.1%
811
 
0.1%
Other values (6)30
 
0.3%
ValueCountFrequency (%)
07484
74.8%
11687
 
16.9%
2461
 
4.6%
3184
 
1.8%
475
 
0.7%
ValueCountFrequency (%)
201
 
< 0.1%
152
 
< 0.1%
132
 
< 0.1%
125
< 0.1%
1111
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9957 
1
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09957
99.5%
148
 
0.5%
2021-02-13T11:52:51.938280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:52.017263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09957
99.5%
148
 
0.5%

Most occurring characters

ValueCountFrequency (%)
09957
99.5%
148
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09957
99.5%
148
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09957
99.5%
148
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09957
99.5%
148
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9824 
1
 
181

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%
2021-02-13T11:52:52.225822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:52.304851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%

Most occurring characters

ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09824
98.2%
1181
 
1.8%

Channel_Discount
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9986 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09986
99.8%
119
 
0.2%
2021-02-13T11:52:52.516592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:52.597399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09986
99.8%
119
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09986
99.8%
119
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09986
99.8%
119
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09986
99.8%
119
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09986
99.8%
119
 
0.2%

Channel_Dual
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9529 
1
 
476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%
2021-02-13T11:52:52.793369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:52.873252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%

Most occurring characters

ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09529
95.2%
1476
 
4.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9765 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%
2021-02-13T11:52:53.091440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:53.171352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%

Most occurring characters

ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09765
97.6%
1240
 
2.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
1
6942 
0
3063 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
16942
69.4%
03063
30.6%
2021-02-13T11:52:53.400033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:53.488612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
16942
69.4%
03063
30.6%

Most occurring characters

ValueCountFrequency (%)
16942
69.4%
03063
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
16942
69.4%
03063
30.6%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
16942
69.4%
03063
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
16942
69.4%
03063
30.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9998 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09998
99.9%
17
 
0.1%
2021-02-13T11:52:53.704381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:53.783206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
10004 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%
2021-02-13T11:52:53.990095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:54.069308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
010004
> 99.9%
11
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
7974 
1
2031 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%
2021-02-13T11:52:54.284944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:54.364006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%

Most occurring characters

ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
07974
79.7%
12031
 
20.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9994 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09994
99.9%
111
 
0.1%
2021-02-13T11:52:54.571255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:54.651446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09994
99.9%
111
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09994
99.9%
111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09994
99.9%
111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09994
99.9%
111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09994
99.9%
111
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9956 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09956
99.5%
149
 
0.5%
2021-02-13T11:52:54.858390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:54.937811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09956
99.5%
149
 
0.5%

Most occurring characters

ValueCountFrequency (%)
09956
99.5%
149
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09956
99.5%
149
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09956
99.5%
149
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09956
99.5%
149
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9998 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09998
99.9%
17
 
0.1%
2021-02-13T11:52:55.146473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:55.226954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09998
99.9%
17
 
0.1%

Sub channel_DCIO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
10002 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%
2021-02-13T11:52:55.438320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:55.516807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
10002 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%
2021-02-13T11:52:55.724985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:55.804022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Sub channel_IBD
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
1
6679 
0
3326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
16679
66.8%
03326
33.2%
2021-02-13T11:52:56.015979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:56.094678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
16679
66.8%
03326
33.2%

Most occurring characters

ValueCountFrequency (%)
16679
66.8%
03326
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
16679
66.8%
03326
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
16679
66.8%
03326
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
16679
66.8%
03326
33.2%

Sub channel_NACS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
6930 
1
3075 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
06930
69.3%
13075
30.7%
2021-02-13T11:52:56.325152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:56.403722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
06930
69.3%
13075
30.7%

Most occurring characters

ValueCountFrequency (%)
06930
69.3%
13075
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
06930
69.3%
13075
30.7%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
06930
69.3%
13075
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
06930
69.3%
13075
30.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
10002 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%
2021-02-13T11:52:56.611833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:56.691322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
010002
> 99.9%
13
 
< 0.1%

Sub channel_RIA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9786 
1
 
219

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%
2021-02-13T11:52:56.897126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:56.975983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%

Most occurring characters

ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09786
97.8%
1219
 
2.2%

Sub channel_USBT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
0
9989 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09989
99.8%
116
 
0.2%
2021-02-13T11:52:57.182215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-13T11:52:57.262578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09989
99.8%
116
 
0.2%

Most occurring characters

ValueCountFrequency (%)
09989
99.8%
116
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10005
100.0%

Most frequent character per category

ValueCountFrequency (%)
09989
99.8%
116
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10005
100.0%

Most frequent character per script

ValueCountFrequency (%)
09989
99.8%
116
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10005
100.0%

Most frequent character per block

ValueCountFrequency (%)
09989
99.8%
116
 
0.2%

Interactions

2021-02-13T11:51:38.418033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:38.572612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:38.703308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:38.833489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:38.967971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.102814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.354451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.493375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.625261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.757729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:39.889171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.022207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.155896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.286631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.419950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.550780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.689115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.818416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:40.958318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.088945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.234231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.374146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.520349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.659987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.806783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:41.953921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.104936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.245993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.395097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.537126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.680733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.825736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:42.971130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.110501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.254598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.403318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.553416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.694605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:43.957848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.102284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.258797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.393329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.535009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.668625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.808074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:44.949572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.093319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.226786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.365300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.503095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.640606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.779894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:45.918849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.055483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.193120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.331432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.475989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.612758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.759128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:46.906512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.066821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.211222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.373404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.541882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.691951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.833543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:47.985541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.114377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.246907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.380091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.513171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.650281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.784380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:48.915065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.048651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.180923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.319002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.450352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.731374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:49.863708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.008829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.148413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.296213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.439419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.579971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.726301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:50.876873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.015453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.158468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.301589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.449228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.594375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.737934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:51.876960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.020198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.163746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.311586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.453742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.606328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.747286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:52.905265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.043651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.193365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.336167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.474027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.619845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.769991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:53.922961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.065405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.207879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.349958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.501196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.645249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.784356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:54.928593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.070818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.221981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.361974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.513333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.666914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.819160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:55.964759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:56.117836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:56.265656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:56.414682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:56.744063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:56.895123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.041534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.198721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.375573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.538773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.712860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:57.866374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.012054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.161192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.308436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.467024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.613473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.776218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:58.924418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.083121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.214261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.351329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.483599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.612484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.747993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:51:59.885850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.027717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.166647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.300735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.434422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.569617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.703220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.833301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:00.971076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.113428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.252159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.383944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.526622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.660517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.804703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:01.940528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.085535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.226682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.360586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.500915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.643192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.789476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:02.928192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.066771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.203314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.376560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.524255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.668092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.821556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:03.963659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.116823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.253514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.401107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.540579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.689712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.825364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:04.971299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:05.110777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:05.456959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:05.605257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:05.747828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:05.909650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.095647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.233358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.370944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.511434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.651361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.787561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:06.940345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-13T11:52:38.608552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:38.747489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:38.896410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.045338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.196354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.354828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.506885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.655489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.814860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:39.970349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.130307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.280304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.433760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.585907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.739803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:40.894047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.048488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.197655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.358199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.510839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.669892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.820473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-13T11:52:41.982995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-13T11:52:57.431127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-13T11:52:58.055065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-13T11:52:58.662927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-13T11:52:59.278338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-13T11:52:59.827843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-13T11:52:42.431267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-13T11:52:44.313052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

no_of_sales_12M_1no_of_Redemption_12M_1no_of_sales_12M_10Kno_of_Redemption_12M_10Kno_of_funds_sold_12M_1no_of_funds_redeemed_12M_1no_of_fund_sales_12M_10Kno_of_funds_Redemption_12M_10Kno_of_assetclass_sold_12M_1no_of_assetclass_redeemed_12M_1no_of_assetclass_sales_12M_10Kno_of_assetclass_Redemption_12M_10KNo_of_fund_currNo_of_asset_currAUMsales_currsales_12Mredemption_currredemption_12Mnew_Fund_added_12Msales_2019new_fund_2019Channel_Asset ManagerChannel_Bank/TrustChannel_DiscountChannel_DualChannel_Fee-Based AdviserChannel_Independent DealerChannel_International OutletChannel_Low/Non ProducerChannel_National Broker-DealerChannel_NetworkerChannel_Private Client GroupSub channel_AffiliatedSub channel_DCIOSub channel_GlobalSub channel_IBDSub channel_NACSSub channel_OtherSub channel_RIASub channel_USBT
021.038.00.01.05.05.00.01.02.02.00.01.08.01.0237480.11250.0019682.000-1496.745-102496.1650000.018633.1050.00000000010000001000
10.00.00.00.00.00.00.00.00.00.00.00.01.01.019629.000.000.0000.0000.0000000.00.0000.00001000000000010000
20.00.00.00.00.00.00.00.00.00.00.00.00.00.01758.700.000.0000.0000.0000000.00.0000.00000010000000000010
320.00.02.00.01.00.01.00.01.00.01.00.01.01.057943.005459.0052484.0000.0000.0000001.093212.0001.00000010000000010000
40.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.000.0000.0000.0000000.00.0000.00000010000000000010
571.055.020.04.07.07.06.04.02.02.02.02.012.02.00.0046936.631274249.925-18231.645-178484.2900002.0467693.0450.00000000010000001000
616.016.02.00.05.05.02.00.03.03.01.00.020.04.02649249.570.00175442.490-20488.060-56948.2900002.0106907.0800.00000000010000001000
76.00.00.00.02.00.00.00.02.00.00.00.05.01.00.000.009737.1100.0000.0000000.02971.7200.00000010000000001000
85.04.00.00.01.01.00.00.01.01.00.00.00.00.00.000.0010060.8000.000-6752.5265790.00.0000.00000000010000001000
91.04.00.01.01.01.00.01.01.01.00.01.03.00.00.000.00981.870-14232.470-62091.9500001.010000.0001.00000010000000001000

Last rows

no_of_sales_12M_1no_of_Redemption_12M_1no_of_sales_12M_10Kno_of_Redemption_12M_10Kno_of_funds_sold_12M_1no_of_funds_redeemed_12M_1no_of_fund_sales_12M_10Kno_of_funds_Redemption_12M_10Kno_of_assetclass_sold_12M_1no_of_assetclass_redeemed_12M_1no_of_assetclass_sales_12M_10Kno_of_assetclass_Redemption_12M_10KNo_of_fund_currNo_of_asset_currAUMsales_currsales_12Mredemption_currredemption_12Mnew_Fund_added_12Msales_2019new_fund_2019Channel_Asset ManagerChannel_Bank/TrustChannel_DiscountChannel_DualChannel_Fee-Based AdviserChannel_Independent DealerChannel_International OutletChannel_Low/Non ProducerChannel_National Broker-DealerChannel_NetworkerChannel_Private Client GroupSub channel_AffiliatedSub channel_DCIOSub channel_GlobalSub channel_IBDSub channel_NACSSub channel_OtherSub channel_RIASub channel_USBT
99950.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0000.0000.0000.0000.000000e+000.00.00000.00000010000000010000
99960.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000.0000.0000.0000.000000e+000.00.00000.00000010000000010000
99971.03.01.01.01.02.01.01.01.02.01.01.03.03.0130553.6200.00023996.160-1500.000-2.411757e+040.043022.96001.00000010000000001000
99988.05.07.01.04.03.04.01.03.02.03.01.08.02.00.00015996.995742943.985-1803.990-2.174908e+041.0667137.66710.00000000010000001000
999910.045.01.00.03.02.01.00.03.02.01.00.00.00.00.0001303.23025421.350-424.380-9.747835e+032.011930.38500.00000010000000001000
1000012.035.07.028.03.04.03.04.02.03.02.03.08.03.0430089.060154026.280208075.030-155432.540-1.701681e+060.0914411.06004.00000010000000010000
1000185.064.01.00.07.04.01.00.03.03.01.00.04.03.0170204.58512611.290165413.245-8412.380-4.004700e+046.0540906.00004.00000010000000010000
1000218.039.05.03.04.04.02.03.02.02.01.01.03.01.00.0000.000226534.495-288519.215-1.657505e+053.0122282.97003.00000000010000001000
1000334.051.05.08.016.020.03.08.05.05.03.03.08.01.00.0006634.500201014.070-8216.830-3.298782e+058.0207464.29000.00000100000000010000
100040.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000.0000.0000.0000.000000e+000.00.00000.00000000010000001000

Duplicate rows

Most frequent

no_of_sales_12M_1no_of_Redemption_12M_1no_of_sales_12M_10Kno_of_Redemption_12M_10Kno_of_funds_sold_12M_1no_of_funds_redeemed_12M_1no_of_fund_sales_12M_10Kno_of_funds_Redemption_12M_10Kno_of_assetclass_sold_12M_1no_of_assetclass_redeemed_12M_1no_of_assetclass_sales_12M_10Kno_of_assetclass_Redemption_12M_10KNo_of_fund_currNo_of_asset_currAUMsales_currsales_12Mredemption_currredemption_12Mnew_Fund_added_12Msales_2019new_fund_2019Channel_Asset ManagerChannel_Bank/TrustChannel_DiscountChannel_DualChannel_Fee-Based AdviserChannel_Independent DealerChannel_International OutletChannel_Low/Non ProducerChannel_National Broker-DealerChannel_NetworkerChannel_Private Client GroupSub channel_AffiliatedSub channel_DCIOSub channel_GlobalSub channel_IBDSub channel_NACSSub channel_OtherSub channel_RIASub channel_USBTcount
50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000000100000000100001122
10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00000000010000001000185
100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00001000000000010000105
40.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000001000000000100091
260.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0000001000000001000079
330.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.0000001000000001000056
80.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000010000000001000046
130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0010000000000001000034
70.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0000010000000000001032
240.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0000000001000000100021